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https://github.com/ml-explore/mlx-examples.git
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6775d6cb3f
* Whisper: rename whisper to mlx_whisper * Whisper: add setup.py config for publish * Whisper: add assets data to setup config * Whisper: pre-commit for setup.py * Whisper: Update README.md * Whisper: Update README.md * nits * fix package data * nit in readme --------- Co-authored-by: Awni Hannun <awni@apple.com>
44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
# Copyright © 2023 Apple Inc.
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import json
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from pathlib import Path
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import mlx.core as mx
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import mlx.nn as nn
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from huggingface_hub import snapshot_download
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from mlx.utils import tree_unflatten
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from . import whisper
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def load_model(
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path_or_hf_repo: str,
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dtype: mx.Dtype = mx.float32,
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) -> whisper.Whisper:
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model_path = Path(path_or_hf_repo)
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if not model_path.exists():
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model_path = Path(snapshot_download(repo_id=path_or_hf_repo))
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with open(str(model_path / "config.json"), "r") as f:
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config = json.loads(f.read())
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config.pop("model_type", None)
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quantization = config.pop("quantization", None)
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model_args = whisper.ModelDimensions(**config)
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weights = mx.load(str(model_path / "weights.npz"))
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model = whisper.Whisper(model_args, dtype)
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if quantization is not None:
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class_predicate = (
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lambda p, m: isinstance(m, (nn.Linear, nn.Embedding))
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and f"{p}.scales" in weights
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)
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nn.quantize(model, **quantization, class_predicate=class_predicate)
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weights = tree_unflatten(list(weights.items()))
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model.update(weights)
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mx.eval(model.parameters())
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return model
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